{"id":"W2114215768","doi":"10.1109/icpr.2000.905626","title":"3D triangular mesh matching through a sequence of registered 2D and 3D images","year":2002,"lang":"en","type":"article","venue":"","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Computer science; Rendering (computer graphics); Computer graphics; Cluster analysis; Virtual reality; Computer vision; Augmented reality; Computer graphics (images); Matching (statistics); Sequence (biology); Image-based modeling and rendering; 3D modeling; Artificial intelligence; Triangle mesh; 3d model; Abstraction; Solid modeling; Graphics; Polygon mesh","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062738,0.0007340036,0.001009482,0.002290689,0.0004190629,0.00182939,0.001205379,0.001036443,0.004373063],"category_scores_gemma":[0.003425675,0.000708146,0.001223457,0.00248196,0.000583611,0.001621396,0.001311406,0.0008945198,0.002165976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000709498,"about_ca_system_score_gemma":0.001538131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006866707,"about_ca_topic_score_gemma":0.008177662,"domain_scores_codex":[0.9989415,0.0001074674,0.00005457255,0.0002707966,0.000520143,0.0001055595],"domain_scores_gemma":[0.9992257,0.0001211542,0.00008235851,0.000304559,0.0002206129,0.00004563702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007825997,0.0001912021,0.003249117,0.0002824773,0.0001300782,0.000496939,0.0005834058,0.114732,0.1803656,0.006845027,0.005226502,0.687115],"study_design_scores_gemma":[0.00003911301,0.0002560566,0.005272761,0.00003278181,0.00008215121,0.0008144503,0.0004068593,0.8917379,0.08131079,0.008129992,0.0118148,0.0001023169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0266226,0.0001034204,0.9691849,0.00008827978,0.00008501288,0.0001450257,0.0002222272,0.002091166,0.001457435],"genre_scores_gemma":[0.1487992,0.0002336957,0.8474148,0.00005199534,0.0000288567,0.00009705581,0.0008594258,0.0004487787,0.002066204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006866707,"threshold_uncertainty_score":0.01462936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06630115811902805,"score_gpt":0.3162918888713385,"score_spread":0.2499907307523105,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}